This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...

In recent years, data-driven soft sensors, especially deep learning soft sensors show great potential for application in the process industry. As a typical deep network, stacked autoencoder (SAE) has ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides. Hossein Abbasi, Mahdi Malekpour, Shahin Yaghoobi, Sina Abdous, Mohammad Hossein Rohban, ...

Specifically, a variational autoencoder firstly trains a generative distribution and extracts reconstruction based features. Then we adopt a deep brief network to estimate the component mixture ...